Overview

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Dataset statistics

Number of variables27
Number of observations179
Missing cells1548
Missing cells (%)32.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.5 KiB
Average record size in memory678.0 B

Variable types

Text1
DateTime3
Categorical7
Numeric16

Dataset

DescriptionEzin 025 - Vital signs monitoring
CreatorRP2 Clinical Data Harmonization Project
URLHEAT Research Projects

Variable descriptions

study_sourceSource study identifier
CD4 cell count (cells/µL)CD4+ T lymphocyte count - immune function indicator
HIV viral load (copies/mL)HIV RNA copies per mL - treatment efficacy marker
Albumin (g/dL)Serum albumin - liver function and nutritional status
primary_datePrimary date of measurement/visit
Age (at enrolment)Patient age at study enrollment

Alerts

BMI (kg/m²) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
Clinical Study ID is highly overall correlated with Patient IDHigh correlation
HEAT_VULNERABILITY_SCORE is highly overall correlated with BMI (kg/m²) and 17 other fieldsHigh correlation
Heart rate (bpm) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
Height is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
Height (m) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
Other measures of obesity is highly overall correlated with BMI (kg/m²) and 5 other fieldsHigh correlation
Patient ID is highly overall correlated with Clinical Study ID and 7 other fieldsHigh correlation
Respiratory rate (breaths/min) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
Temperature (°C) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
Weight (kg) is highly overall correlated with BMI (kg/m²) and 5 other fieldsHigh correlation
coordinate_source is highly overall correlated with HEAT_VULNERABILITY_SCORE and 4 other fieldsHigh correlation
heart rate is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
jhb_subregion is highly overall correlated with BMI (kg/m²) and 14 other fieldsHigh correlation
longitude is highly overall correlated with BMI (kg/m²) and 14 other fieldsHigh correlation
month is highly overall correlated with HEAT_VULNERABILITY_SCORE and 2 other fieldsHigh correlation
oral temperature is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
original_record_index is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
respiration rate is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
season is highly overall correlated with HEAT_VULNERABILITY_SCORE and 4 other fieldsHigh correlation
weight is highly overall correlated with BMI (kg/m²) and 5 other fieldsHigh correlation
year is highly overall correlated with HEAT_VULNERABILITY_SCORE and 3 other fieldsHigh correlation
longitude is highly imbalanced (84.6%)Imbalance
jhb_subregion is highly imbalanced (84.6%)Imbalance
heart rate has 129 (72.1%) missing valuesMissing
weight has 129 (72.1%) missing valuesMissing
Height has 129 (72.1%) missing valuesMissing
oral temperature has 129 (72.1%) missing valuesMissing
respiration rate has 129 (72.1%) missing valuesMissing
Other measures of obesity has 129 (72.1%) missing valuesMissing
BMI (kg/m²) has 129 (72.1%) missing valuesMissing
Height (m) has 129 (72.1%) missing valuesMissing
Weight (kg) has 129 (72.1%) missing valuesMissing
Temperature (°C) has 129 (72.1%) missing valuesMissing
Heart rate (bpm) has 129 (72.1%) missing valuesMissing
Respiratory rate (breaths/min) has 129 (72.1%) missing valuesMissing
anonymous_patient_id has unique valuesUnique
Patient ID has unique valuesUnique
original_record_index has unique valuesUnique

Reproduction

Analysis started2025-11-11 10:50:50.796632
Analysis finished2025-11-11 10:55:33.568605
Duration4 minutes and 42.77 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2025-11-11T12:55:33.912209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters3043
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)100.0%

Sample

1st rowHEAT_86780BEB8207
2nd rowHEAT_E41FAC8BEC53
3rd rowHEAT_4FB589B26473
4th rowHEAT_B5EFC15EA0E1
5th rowHEAT_AE8DDF94E146
ValueCountFrequency (%)
heat_86780beb82071
 
0.6%
heat_e41fac8bec531
 
0.6%
heat_4fb589b264731
 
0.6%
heat_b5efc15ea0e11
 
0.6%
heat_ae8ddf94e1461
 
0.6%
heat_a8d62c35de7e1
 
0.6%
heat_b9673d69aded1
 
0.6%
heat_3b7cbb8cbf9d1
 
0.6%
heat_8d23552d6add1
 
0.6%
heat_55dcd595bb951
 
0.6%
Other values (169)169
94.4%
2025-11-11T12:55:35.139692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E322
 
10.6%
A313
 
10.3%
H179
 
5.9%
T179
 
5.9%
_179
 
5.9%
9147
 
4.8%
0143
 
4.7%
4139
 
4.6%
C139
 
4.6%
5138
 
4.5%
Other values (9)1165
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E322
 
10.6%
A313
 
10.3%
H179
 
5.9%
T179
 
5.9%
_179
 
5.9%
9147
 
4.8%
0143
 
4.7%
4139
 
4.6%
C139
 
4.6%
5138
 
4.5%
Other values (9)1165
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E322
 
10.6%
A313
 
10.3%
H179
 
5.9%
T179
 
5.9%
_179
 
5.9%
9147
 
4.8%
0143
 
4.7%
4139
 
4.6%
C139
 
4.6%
5138
 
4.5%
Other values (9)1165
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E322
 
10.6%
A313
 
10.3%
H179
 
5.9%
T179
 
5.9%
_179
 
5.9%
9147
 
4.8%
0143
 
4.7%
4139
 
4.6%
C139
 
4.6%
5138
 
4.5%
Other values (9)1165
38.3%

primary_date
Date

Primary date of measurement/visit

Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:55:35.576997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:36.717879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2021
148 
2020
31 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters716
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021148
82.7%
202031
 
17.3%

Length

2025-11-11T12:55:38.158848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:38.692986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2021148
82.7%
202031
 
17.3%

Most occurring characters

ValueCountFrequency (%)
2358
50.0%
0210
29.3%
1148
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2358
50.0%
0210
29.3%
1148
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2358
50.0%
0210
29.3%
1148
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2358
50.0%
0210
29.3%
1148
20.7%

month
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4860335
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:39.215870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0434264
Coefficient of variation (CV)0.55475899
Kurtosis-0.70995305
Mean5.4860335
Median Absolute Deviation (MAD)1
Skewness0.077888098
Sum982
Variance9.2624443
MonotonicityNot monotonic
2025-11-11T12:55:39.663810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
650
27.9%
731
17.3%
129
16.2%
215
 
8.4%
312
 
6.7%
1012
 
6.7%
510
 
5.6%
99
 
5.0%
115
 
2.8%
125
 
2.8%
ValueCountFrequency (%)
129
16.2%
215
 
8.4%
312
 
6.7%
41
 
0.6%
510
 
5.6%
650
27.9%
731
17.3%
99
 
5.0%
1012
 
6.7%
115
 
2.8%
ValueCountFrequency (%)
125
 
2.8%
115
 
2.8%
1012
 
6.7%
99
 
5.0%
731
17.3%
650
27.9%
510
 
5.6%
41
 
0.6%
312
 
6.7%
215
 
8.4%

season
Categorical

High correlation 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Winter
81 
Summer
49 
Spring
26 
Autumn
23 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Winter81
45.3%
Summer49
27.4%
Spring26
 
14.5%
Autumn23
 
12.8%

Length

2025-11-11T12:55:40.302011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:40.984281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
winter81
45.3%
summer49
27.4%
spring26
 
14.5%
autumn23
 
12.8%

Most occurring characters

ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

longitude
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
28.0473
175 
27.9394
 
4

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1253
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Length

2025-11-11T12:55:42.004039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:42.557435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2179
14.3%
.179
14.3%
7179
14.3%
4179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
7179
14.3%
4179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
7179
14.3%
4179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
7179
14.3%
4179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

jhb_subregion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Central_JHB
175 
Western_JHB
 
4

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1969
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB175
97.8%
Western_JHB4
 
2.2%

Length

2025-11-11T12:55:43.243814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:43.821963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb175
97.8%
western_jhb4
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e183
9.3%
t179
9.1%
n179
9.1%
r179
9.1%
J179
9.1%
B179
9.1%
_179
9.1%
H179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e183
9.3%
t179
9.1%
n179
9.1%
r179
9.1%
J179
9.1%
B179
9.1%
_179
9.1%
H179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e183
9.3%
t179
9.1%
n179
9.1%
r179
9.1%
J179
9.1%
B179
9.1%
_179
9.1%
H179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e183
9.3%
t179
9.1%
n179
9.1%
r179
9.1%
J179
9.1%
B179
9.1%
_179
9.1%
H179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

heart rate
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)68.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean78
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:44.336317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.45
Q170.25
median76
Q386
95-th percentile102.2
Maximum110
Range60
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.810673
Coefficient of variation (CV)0.17705991
Kurtosis-0.15961177
Mean78
Median Absolute Deviation (MAD)9
Skewness0.20889091
Sum3900
Variance190.73469
MonotonicityNot monotonic
2025-11-11T12:55:44.793689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
744
 
2.2%
723
 
1.7%
673
 
1.7%
832
 
1.1%
842
 
1.1%
962
 
1.1%
732
 
1.1%
862
 
1.1%
642
 
1.1%
802
 
1.1%
Other values (24)26
 
14.5%
(Missing)129
72.1%
ValueCountFrequency (%)
501
 
0.6%
511
 
0.6%
551
 
0.6%
561
 
0.6%
601
 
0.6%
631
 
0.6%
642
1.1%
661
 
0.6%
673
1.7%
701
 
0.6%
ValueCountFrequency (%)
1101
0.6%
1051
0.6%
1041
0.6%
1001
0.6%
991
0.6%
962
1.1%
941
0.6%
911
0.6%
891
0.6%
881
0.6%

weight
Real number (ℝ)

High correlation  Missing 

Distinct47
Distinct (%)94.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean74.736
Minimum49.9
Maximum117.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:45.334573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.9
5-th percentile50.85
Q160.55
median73.1
Q384.9
95-th percentile107.7
Maximum117.8
Range67.9
Interquartile range (IQR)24.35

Descriptive statistics

Standard deviation17.108761
Coefficient of variation (CV)0.22892262
Kurtosis-0.14569641
Mean74.736
Median Absolute Deviation (MAD)12.05
Skewness0.57554181
Sum3736.8
Variance292.7097
MonotonicityNot monotonic
2025-11-11T12:55:45.868709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
902
 
1.1%
70.92
 
1.1%
73.12
 
1.1%
68.91
 
0.6%
112.41
 
0.6%
58.21
 
0.6%
61.61
 
0.6%
109.51
 
0.6%
501
 
0.6%
100.11
 
0.6%
Other values (37)37
 
20.7%
(Missing)129
72.1%
ValueCountFrequency (%)
49.91
0.6%
501
0.6%
50.41
0.6%
51.41
0.6%
53.81
0.6%
54.51
0.6%
54.61
0.6%
551
0.6%
56.21
0.6%
58.21
0.6%
ValueCountFrequency (%)
117.81
0.6%
112.41
0.6%
109.51
0.6%
105.51
0.6%
100.11
0.6%
97.31
0.6%
91.91
0.6%
902
1.1%
89.61
0.6%
88.41
0.6%

Height
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)52.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.681
Minimum1.52
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:46.382398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile1.559
Q11.62
median1.68
Q31.75
95-th percentile1.79
Maximum1.87
Range0.35
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.080311892
Coefficient of variation (CV)0.047776259
Kurtosis-0.52690869
Mean1.681
Median Absolute Deviation (MAD)0.07
Skewness0.19250412
Sum84.05
Variance0.00645
MonotonicityNot monotonic
2025-11-11T12:55:46.900328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.614
 
2.2%
1.724
 
2.2%
1.754
 
2.2%
1.683
 
1.7%
1.653
 
1.7%
1.643
 
1.7%
1.793
 
1.7%
1.772
 
1.1%
1.692
 
1.1%
1.552
 
1.1%
Other values (16)20
 
11.2%
(Missing)129
72.1%
ValueCountFrequency (%)
1.521
 
0.6%
1.552
1.1%
1.571
 
0.6%
1.582
1.1%
1.591
 
0.6%
1.61
 
0.6%
1.614
2.2%
1.622
1.1%
1.632
1.1%
1.643
1.7%
ValueCountFrequency (%)
1.871
 
0.6%
1.851
 
0.6%
1.793
1.7%
1.781
 
0.6%
1.772
1.1%
1.762
1.1%
1.754
2.2%
1.731
 
0.6%
1.724
2.2%
1.711
 
0.6%

oral temperature
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)34.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean36.488
Minimum35.2
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:47.479497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile35.9
Q136.2
median36.45
Q336.7
95-th percentile37.265
Maximum37.7
Range2.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46582821
Coefficient of variation (CV)0.012766614
Kurtosis0.904937
Mean36.488
Median Absolute Deviation (MAD)0.25
Skewness0.028784237
Sum1824.4
Variance0.21699592
MonotonicityNot monotonic
2025-11-11T12:55:48.062017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36.77
 
3.9%
36.47
 
3.9%
36.55
 
2.8%
364
 
2.2%
36.34
 
2.2%
36.13
 
1.7%
36.23
 
1.7%
36.83
 
1.7%
373
 
1.7%
35.92
 
1.1%
Other values (7)9
 
5.0%
(Missing)129
72.1%
ValueCountFrequency (%)
35.21
 
0.6%
35.51
 
0.6%
35.92
 
1.1%
364
2.2%
36.13
1.7%
36.23
1.7%
36.34
2.2%
36.47
3.9%
36.55
2.8%
36.61
 
0.6%
ValueCountFrequency (%)
37.71
 
0.6%
37.42
 
1.1%
37.12
 
1.1%
373
1.7%
36.91
 
0.6%
36.83
1.7%
36.77
3.9%
36.61
 
0.6%
36.55
2.8%
36.47
3.9%

Patient ID
Real number (ℝ)

High correlation  Unique 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2207.7709
Minimum1025
Maximum5029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:48.655628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1025
5-th percentile1104.9
Q11347.5
median2244
Q33017.5
95-th percentile3194.3
Maximum5029
Range4004
Interquartile range (IQR)1670

Descriptive statistics

Standard deviation890.42719
Coefficient of variation (CV)0.40331502
Kurtosis1.2463502
Mean2207.7709
Median Absolute Deviation (MAD)868
Skewness0.88405619
Sum395191
Variance792860.58
MonotonicityStrictly increasing
2025-11-11T12:55:49.342607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10251
 
0.6%
10581
 
0.6%
10601
 
0.6%
10631
 
0.6%
10661
 
0.6%
10991
 
0.6%
11021
 
0.6%
11031
 
0.6%
11041
 
0.6%
11051
 
0.6%
Other values (169)169
94.4%
ValueCountFrequency (%)
10251
0.6%
10581
0.6%
10601
0.6%
10631
0.6%
10661
0.6%
10991
0.6%
11021
0.6%
11031
0.6%
11041
0.6%
11051
0.6%
ValueCountFrequency (%)
50291
0.6%
50281
0.6%
50261
0.6%
50201
0.6%
50111
0.6%
50031
0.6%
32021
0.6%
32011
0.6%
31971
0.6%
31941
0.6%

original_record_index
Real number (ℝ)

High correlation  Unique 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.01676
Minimum0
Maximum191
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:50.056828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.9
Q152.5
median102
Q3146.5
95-th percentile182.1
Maximum191
Range191
Interquartile range (IQR)94

Descriptive statistics

Standard deviation55.837593
Coefficient of variation (CV)0.56392062
Kurtosis-1.1527571
Mean99.01676
Median Absolute Deviation (MAD)47
Skewness-0.13315843
Sum17724
Variance3117.8368
MonotonicityStrictly increasing
2025-11-11T12:55:50.618286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.6%
11
 
0.6%
21
 
0.6%
31
 
0.6%
41
 
0.6%
51
 
0.6%
61
 
0.6%
71
 
0.6%
81
 
0.6%
91
 
0.6%
Other values (169)169
94.4%
ValueCountFrequency (%)
01
0.6%
11
0.6%
21
0.6%
31
0.6%
41
0.6%
51
0.6%
61
0.6%
71
0.6%
81
0.6%
91
0.6%
ValueCountFrequency (%)
1911
0.6%
1901
0.6%
1891
0.6%
1881
0.6%
1871
0.6%
1861
0.6%
1851
0.6%
1841
0.6%
1831
0.6%
1821
0.6%

date
Date

Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:55:51.379710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:52.503642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Clinical Study ID
Categorical

High correlation 

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
Arm B
39 
Arm A
36 
Arm D
36 
Arm C
35 
Arm E
33 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters895
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArm A
2nd rowArm B
3rd rowArm A
4th rowArm C
5th rowArm A

Common Values

ValueCountFrequency (%)
Arm B39
21.8%
Arm A36
20.1%
Arm D36
20.1%
Arm C35
19.6%
Arm E33
18.4%

Length

2025-11-11T12:55:53.695377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:54.379654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
arm179
50.0%
b39
 
10.9%
a36
 
10.1%
d36
 
10.1%
c35
 
9.8%
e33
 
9.2%

Most occurring characters

ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Potassium (mEq/L)
Real number (ℝ)

Distinct30
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8128492
Minimum3.5
Maximum6.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:55.164783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.9
Q14.4
median4.8
Q35.1
95-th percentile5.9
Maximum6.6
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.60670619
Coefficient of variation (CV)0.12605967
Kurtosis0.32414825
Mean4.8128492
Median Absolute Deviation (MAD)0.4
Skewness0.53010157
Sum861.5
Variance0.3680924
MonotonicityNot monotonic
2025-11-11T12:55:55.603837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.716
 
8.9%
4.915
 
8.4%
4.815
 
8.4%
4.313
 
7.3%
4.411
 
6.1%
4.610
 
5.6%
5.110
 
5.6%
59
 
5.0%
5.29
 
5.0%
4.18
 
4.5%
Other values (20)63
35.2%
ValueCountFrequency (%)
3.51
 
0.6%
3.61
 
0.6%
3.72
 
1.1%
3.83
 
1.7%
3.96
3.4%
42
 
1.1%
4.18
4.5%
4.26
3.4%
4.313
7.3%
4.411
6.1%
ValueCountFrequency (%)
6.62
 
1.1%
6.41
 
0.6%
6.31
 
0.6%
6.23
1.7%
6.11
 
0.6%
5.93
1.7%
5.82
 
1.1%
5.75
2.8%
5.63
1.7%
5.56
3.4%

respiration rate
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:56.209086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-11T12:55:56.633236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

Other measures of obesity
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:55:57.135945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-11T12:55:57.642364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.822065981
 
0.6%
18.577037251
 
0.6%
19.829481891
 
0.6%
25.307621671
 
0.6%
32.841490141
 
0.6%
17.506389831
 
0.6%
31.313449051
 
0.6%
31.633714881
 
0.6%
39.10156251
 
0.6%
28.201072491
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

coordinate_source
Categorical

High correlation 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
JHB_EZIN_025
122 
JHB_VIDA_008
53 
JHB_SCHARP_006
 
4

Length

Max length14
Median length12
Mean length12.044693
Min length12

Characters and Unicode

Total characters2156
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_VIDA_008
2nd rowJHB_VIDA_008
3rd rowJHB_VIDA_008
4th rowJHB_VIDA_008
5th rowJHB_VIDA_008

Common Values

ValueCountFrequency (%)
JHB_EZIN_025122
68.2%
JHB_VIDA_00853
29.6%
JHB_SCHARP_0064
 
2.2%

Length

2025-11-11T12:55:58.502721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:55:59.247727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_025122
68.2%
jhb_vida_00853
29.6%
jhb_scharp_0064
 
2.2%

Most occurring characters

ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
Z122
 
5.7%
E122
 
5.7%
N122
 
5.7%
2122
 
5.7%
Other values (10)358
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
Z122
 
5.7%
E122
 
5.7%
N122
 
5.7%
2122
 
5.7%
Other values (10)358
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
Z122
 
5.7%
E122
 
5.7%
N122
 
5.7%
2122
 
5.7%
Other values (10)358
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
Z122
 
5.7%
E122
 
5.7%
N122
 
5.7%
2122
 
5.7%
Other values (10)358
16.6%
Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:56:00.173269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:56:01.255944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HEAT_VULNERABILITY_SCORE
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
129 
0.2624535785467304
50 

Length

Max length18
Median length3
Mean length7.1899441
Min length3

Characters and Unicode

Total characters1287
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.2624535785467304
2nd row0.2624535785467304
3rd row0.2624535785467304
4th row0.2624535785467304
5th row0.2624535785467304

Common Values

ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Length

2025-11-11T12:56:02.408034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:56:02.868745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Most occurring characters

ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

BMI (kg/m²)
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:03.429188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-11T12:56:03.946001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.822065981
 
0.6%
18.577037251
 
0.6%
19.829481891
 
0.6%
25.307621671
 
0.6%
32.841490141
 
0.6%
17.506389831
 
0.6%
31.313449051
 
0.6%
31.633714881
 
0.6%
39.10156251
 
0.6%
28.201072491
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

Height (m)
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)52.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.681
Minimum1.52
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:04.514549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile1.559
Q11.62
median1.68
Q31.75
95-th percentile1.79
Maximum1.87
Range0.35
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.080311892
Coefficient of variation (CV)0.047776259
Kurtosis-0.52690869
Mean1.681
Median Absolute Deviation (MAD)0.07
Skewness0.19250412
Sum84.05
Variance0.00645
MonotonicityNot monotonic
2025-11-11T12:56:05.011843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.614
 
2.2%
1.724
 
2.2%
1.754
 
2.2%
1.683
 
1.7%
1.653
 
1.7%
1.643
 
1.7%
1.793
 
1.7%
1.772
 
1.1%
1.692
 
1.1%
1.552
 
1.1%
Other values (16)20
 
11.2%
(Missing)129
72.1%
ValueCountFrequency (%)
1.521
 
0.6%
1.552
1.1%
1.571
 
0.6%
1.582
1.1%
1.591
 
0.6%
1.61
 
0.6%
1.614
2.2%
1.622
1.1%
1.632
1.1%
1.643
1.7%
ValueCountFrequency (%)
1.871
 
0.6%
1.851
 
0.6%
1.793
1.7%
1.781
 
0.6%
1.772
1.1%
1.762
1.1%
1.754
2.2%
1.731
 
0.6%
1.724
2.2%
1.711
 
0.6%

Weight (kg)
Real number (ℝ)

High correlation  Missing 

Distinct47
Distinct (%)94.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean74.736
Minimum49.9
Maximum117.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:05.600392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.9
5-th percentile50.85
Q160.55
median73.1
Q384.9
95-th percentile107.7
Maximum117.8
Range67.9
Interquartile range (IQR)24.35

Descriptive statistics

Standard deviation17.108761
Coefficient of variation (CV)0.22892262
Kurtosis-0.14569641
Mean74.736
Median Absolute Deviation (MAD)12.05
Skewness0.57554181
Sum3736.8
Variance292.7097
MonotonicityNot monotonic
2025-11-11T12:56:06.092637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
902
 
1.1%
70.92
 
1.1%
73.12
 
1.1%
68.91
 
0.6%
112.41
 
0.6%
58.21
 
0.6%
61.61
 
0.6%
109.51
 
0.6%
501
 
0.6%
100.11
 
0.6%
Other values (37)37
 
20.7%
(Missing)129
72.1%
ValueCountFrequency (%)
49.91
0.6%
501
0.6%
50.41
0.6%
51.41
0.6%
53.81
0.6%
54.51
0.6%
54.61
0.6%
551
0.6%
56.21
0.6%
58.21
0.6%
ValueCountFrequency (%)
117.81
0.6%
112.41
0.6%
109.51
0.6%
105.51
0.6%
100.11
0.6%
97.31
0.6%
91.91
0.6%
902
1.1%
89.61
0.6%
88.41
0.6%

Temperature (°C)
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)34.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean36.488
Minimum35.2
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:06.605842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile35.9
Q136.2
median36.45
Q336.7
95-th percentile37.265
Maximum37.7
Range2.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46582821
Coefficient of variation (CV)0.012766614
Kurtosis0.904937
Mean36.488
Median Absolute Deviation (MAD)0.25
Skewness0.028784237
Sum1824.4
Variance0.21699592
MonotonicityNot monotonic
2025-11-11T12:56:07.174083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36.77
 
3.9%
36.47
 
3.9%
36.55
 
2.8%
364
 
2.2%
36.34
 
2.2%
36.13
 
1.7%
36.23
 
1.7%
36.83
 
1.7%
373
 
1.7%
35.92
 
1.1%
Other values (7)9
 
5.0%
(Missing)129
72.1%
ValueCountFrequency (%)
35.21
 
0.6%
35.51
 
0.6%
35.92
 
1.1%
364
2.2%
36.13
1.7%
36.23
1.7%
36.34
2.2%
36.47
3.9%
36.55
2.8%
36.61
 
0.6%
ValueCountFrequency (%)
37.71
 
0.6%
37.42
 
1.1%
37.12
 
1.1%
373
1.7%
36.91
 
0.6%
36.83
1.7%
36.77
3.9%
36.61
 
0.6%
36.55
2.8%
36.47
3.9%

Heart rate (bpm)
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)68.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean78
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:07.682641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.45
Q170.25
median76
Q386
95-th percentile102.2
Maximum110
Range60
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.810673
Coefficient of variation (CV)0.17705991
Kurtosis-0.15961177
Mean78
Median Absolute Deviation (MAD)9
Skewness0.20889091
Sum3900
Variance190.73469
MonotonicityNot monotonic
2025-11-11T12:56:08.132084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
744
 
2.2%
723
 
1.7%
673
 
1.7%
832
 
1.1%
842
 
1.1%
962
 
1.1%
732
 
1.1%
862
 
1.1%
642
 
1.1%
802
 
1.1%
Other values (24)26
 
14.5%
(Missing)129
72.1%
ValueCountFrequency (%)
501
 
0.6%
511
 
0.6%
551
 
0.6%
561
 
0.6%
601
 
0.6%
631
 
0.6%
642
1.1%
661
 
0.6%
673
1.7%
701
 
0.6%
ValueCountFrequency (%)
1101
0.6%
1051
0.6%
1041
0.6%
1001
0.6%
991
0.6%
962
1.1%
941
0.6%
911
0.6%
891
0.6%
881
0.6%

Respiratory rate (breaths/min)
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-11T12:56:08.629095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-11T12:56:09.063338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

Interactions

2025-11-11T12:55:07.667469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:53.256667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:08.239240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:20.113161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:32.386610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:46.053737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:59.473788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:59.116086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:17.814035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:35.855843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:49.404966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:02.845286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:14.761803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:28.502441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:41.552440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:54.888226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:08.385827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:53.569539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:08.724597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:20.672751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:32.987150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:46.647992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:06.309372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:59.799490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:18.421016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:36.462008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:49.994723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:03.357433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:15.375529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:29.059174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:42.149141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:55.465615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:09.155875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:54.082427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:09.212395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:21.244622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:33.701156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:47.343093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:08.598192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:00.563199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:19.097800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:37.161230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:50.708201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:03.942891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:16.088114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:29.666891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:42.858887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:56.066935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:09.965096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:54.604913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:09.754699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:21.799163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:34.637631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:48.049233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:10.854578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:01.307544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:19.789117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:37.873352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:51.414402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:04.541239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:16.814276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:30.307494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:43.560178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:56.672732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:10.859754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:55.185372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:10.426298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:22.489172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:35.259446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:48.786563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:13.188522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:02.131841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:20.517435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:38.640113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:52.175603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:05.227090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:17.478918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:31.046040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:44.333517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:57.402650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:11.715080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:50:55.761091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:11.087192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:23.181209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:36.014233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:49.402015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:15.586524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:02.973923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:21.265257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:39.451386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:52.938506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:05.894142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:18.239446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:31.792488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:45.022689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:58.156266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:14.214248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:02.355546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:13.271245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:25.508318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:38.361409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:51.783174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:25.981778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:09.802378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:53:41.838355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:55.313126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:08.190675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:20.649549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:34.084117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:52:33.387527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:54:21.521193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:34.851223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:55:01.269471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:16.025107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:03.633403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:14.631471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:51:39.959486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:53.396310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:40.399131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:53:29.570935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:43.437593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:56.895855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:09.593076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:54:48.939905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:51:15.351954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:51:40.735052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:54.178956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:42.768273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:12.236472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:30.326879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:44.082056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:57.675088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:10.262480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:23.117118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:36.594845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:49.729449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:02.738311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:51:04.812334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:52:47.430961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:13.759895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:31.992079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:45.544359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:58.931682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:55:19.427511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:51:17.386627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:29.655506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:52:49.828215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:53:46.339707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:53:59.733276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:12.054039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:25.277964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:55:04.864866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:20.288474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:06.428443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:18.001553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:30.252368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:43.634647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:51:57.160418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:52:52.147955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:53:47.097790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:54:01.976427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:13.994075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:27.604455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:40.699081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:54:54.018559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:55:06.820071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T12:56:09.511953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BMI (kg/m²)Clinical Study IDHEAT_VULNERABILITY_SCOREHeart rate (bpm)HeightHeight (m)Other measures of obesityPatient IDPotassium (mEq/L)Respiratory rate (breaths/min)Temperature (°C)Weight (kg)coordinate_sourceheart ratejhb_subregionlongitudemonthoral temperatureoriginal_record_indexrespiration rateseasonweightyear
BMI (kg/m²)1.0000.0001.0000.187-0.298-0.2981.000-0.0030.1170.005-0.1260.9100.0000.1871.0001.000-0.248-0.126-0.0030.0050.0000.9100.000
Clinical Study ID0.0001.0000.0000.0000.0000.0000.0001.0000.1090.0000.0940.0620.0000.0000.0000.0000.0000.0940.0000.0000.0000.0620.000
HEAT_VULNERABILITY_SCORE1.0000.0001.0001.0001.0001.0001.0001.0000.1281.0001.0001.0000.7091.0000.0000.0000.8121.0000.6561.0000.8041.0000.717
Heart rate (bpm)0.1870.0001.0001.000-0.140-0.1400.1870.017-0.275-0.0200.2940.1230.0001.0001.0001.0000.0840.2940.017-0.0200.0000.1230.000
Height-0.2980.0001.000-0.1401.0001.000-0.2980.0190.160-0.014-0.0970.0910.000-0.1401.0001.0000.220-0.0970.019-0.0140.0800.0910.206
Height (m)-0.2980.0001.000-0.1401.0001.000-0.2980.0190.160-0.014-0.0970.0910.000-0.1401.0001.0000.220-0.0970.019-0.0140.0800.0910.206
Other measures of obesity1.0000.0001.0000.187-0.298-0.2981.000-0.0030.1170.005-0.1260.9100.0000.1871.0001.000-0.248-0.126-0.0030.0050.0000.9100.000
Patient ID-0.0031.0001.0000.0170.0190.019-0.0031.000-0.013-0.386-0.1380.0151.0000.0171.0001.0000.106-0.1381.000-0.3861.0000.0151.000
Potassium (mEq/L)0.1170.1090.128-0.2750.1600.1600.117-0.0131.000-0.020-0.2680.1820.000-0.2750.0000.000-0.055-0.268-0.013-0.0200.1320.1820.058
Respiratory rate (breaths/min)0.0050.0001.000-0.020-0.014-0.0140.005-0.386-0.0201.0000.3270.0080.000-0.0201.0001.000-0.0590.327-0.3861.0000.1690.0080.114
Temperature (°C)-0.1260.0941.0000.294-0.097-0.097-0.126-0.138-0.2680.3271.000-0.1420.0000.2941.0001.000-0.2881.000-0.1380.3270.218-0.1420.253
Weight (kg)0.9100.0621.0000.1230.0910.0910.9100.0150.1820.008-0.1421.0000.2290.1231.0001.000-0.193-0.1420.0150.0080.3021.0000.228
coordinate_source0.0000.0000.7090.0000.0000.0000.0001.0000.0000.0000.0000.2291.0000.0000.9970.9970.4200.0000.5040.0000.4410.2290.436
heart rate0.1870.0001.0001.000-0.140-0.1400.1870.017-0.275-0.0200.2940.1230.0001.0001.0001.0000.0840.2940.017-0.0200.0000.1230.000
jhb_subregion1.0000.0000.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.9971.0001.0000.8710.0001.0000.3141.0000.1041.0000.000
longitude1.0000.0000.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.9971.0000.8711.0000.0001.0000.3141.0000.1041.0000.000
month-0.2480.0000.8120.0840.2200.220-0.2480.106-0.055-0.059-0.288-0.1930.4200.0840.0000.0001.000-0.2880.106-0.0590.960-0.1930.980
oral temperature-0.1260.0941.0000.294-0.097-0.097-0.126-0.138-0.2680.3271.000-0.1420.0000.2941.0001.000-0.2881.000-0.1380.3270.218-0.1420.253
original_record_index-0.0030.0000.6560.0170.0190.019-0.0031.000-0.013-0.386-0.1380.0150.5040.0170.3140.3140.106-0.1381.000-0.3860.5900.0150.456
respiration rate0.0050.0001.000-0.020-0.014-0.0140.005-0.386-0.0201.0000.3270.0080.000-0.0201.0001.000-0.0590.327-0.3861.0000.1690.0080.114
season0.0000.0000.8040.0000.0800.0800.0001.0000.1320.1690.2180.3020.4410.0000.1040.1040.9600.2180.5900.1691.0000.3020.901
weight0.9100.0621.0000.1230.0910.0910.9100.0150.1820.008-0.1421.0000.2290.1231.0001.000-0.193-0.1420.0150.0080.3021.0000.228
year0.0000.0000.7170.0000.2060.2060.0001.0000.0580.1140.2530.2280.4360.0000.0000.0000.9800.2530.4560.1140.9010.2281.000

Missing values

2025-11-11T12:55:22.964995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T12:55:26.545966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-11T12:55:30.856445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anonymous_patient_idprimary_dateyearmonthseasonlongitudejhb_subregionheart rateweightHeightoral temperaturePatient IDoriginal_record_indexdateClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesitycoordinate_sourceprimary_date_parsedHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)
2779HEAT_86780BEB82072020-10-15202010Spring28.0473Central_JHB83.058.21.7736.510250.02020-10-15Arm A3.520.018.577037JHB_VIDA_0082020-10-150.26245418.5770371.7758.236.583.020.0
2780HEAT_E41FAC8BEC532020-10-28202010Spring28.0473Central_JHB72.061.61.7236.110581.02020-10-28Arm B4.622.020.822066JHB_VIDA_0082020-10-280.26245420.8220661.7261.636.172.022.0
2781HEAT_4FB589B264732020-10-29202010Spring28.0473Central_JHB84.060.01.6436.310602.02020-10-29Arm A4.919.022.308150JHB_VIDA_0082020-10-290.26245422.3081501.6460.036.384.019.0
2782HEAT_B5EFC15EA0E12020-11-04202011Spring28.0473Central_JHB74.051.41.6136.710633.02020-11-04Arm C4.319.019.829482JHB_VIDA_0082020-11-040.26245419.8294821.6151.436.774.019.0
2783HEAT_AE8DDF94E1462020-11-05202011Spring28.0473Central_JHB105.068.91.6537.110664.02020-11-05Arm A4.720.025.307622JHB_VIDA_0082020-11-050.26245425.3076221.6568.937.1105.020.0
2784HEAT_A8D62C35DE7E2020-12-09202012Summer28.0473Central_JHB96.0112.41.8536.310995.02020-12-09Arm B4.918.032.841490JHB_VIDA_0082020-12-090.26245432.8414901.85112.436.396.018.0
2785HEAT_B9673D69ADED2020-12-11202012Summer28.0473Central_JHB64.050.01.6937.011026.02020-12-11Arm E4.419.017.506390JHB_VIDA_0082020-12-110.26245417.5063901.6950.037.064.019.0
2786HEAT_3B7CBB8CBF9D2020-12-15202012Summer28.0473Central_JHB50.0109.51.8736.211037.02020-12-15Arm D4.918.031.313449JHB_VIDA_0082020-12-150.26245431.3134491.87109.536.250.018.0
2787HEAT_8D23552D6ADD2021-01-0520211Summer28.0473Central_JHB80.076.01.5536.411048.02021-01-05Arm B4.419.031.633715JHB_VIDA_0082021-01-050.26245431.6337151.5576.036.480.019.0
2788HEAT_55DCD595BB952021-01-0520211Summer28.0473Central_JHB70.0100.11.6036.311059.02021-01-05Arm C4.120.039.101562JHB_VIDA_0082021-01-050.26245439.1015621.60100.136.370.020.0
anonymous_patient_idprimary_dateyearmonthseasonlongitudejhb_subregionheart rateweightHeightoral temperaturePatient IDoriginal_record_indexdateClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesitycoordinate_sourceprimary_date_parsedHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)
2948HEAT_E02B5241C57E2021-06-1520216Winter28.0473Central_JHBNaNNaNNaNNaN3194182.02021-06-15Arm B4.2NaNNaNJHB_EZIN_0252021-06-150.0NaNNaNNaNNaNNaNNaN
2949HEAT_26FBD811F2082021-06-1620216Winter28.0473Central_JHBNaNNaNNaNNaN3197183.02021-06-16Arm B5.1NaNNaNJHB_EZIN_0252021-06-160.0NaNNaNNaNNaNNaNNaN
2950HEAT_43B3F69469222021-07-0120217Winter28.0473Central_JHBNaNNaNNaNNaN3201184.02021-07-01Arm E4.7NaNNaNJHB_EZIN_0252021-07-010.0NaNNaNNaNNaNNaNNaN
2951HEAT_B0E4C1B632012021-07-0120217Winter28.0473Central_JHBNaNNaNNaNNaN3202185.02021-07-01Arm D3.8NaNNaNJHB_EZIN_0252021-07-010.0NaNNaNNaNNaNNaNNaN
2952HEAT_1CB8262A36592021-05-1320215Autumn28.0473Central_JHBNaNNaNNaNNaN5003186.02021-05-13Arm A5.6NaNNaNJHB_VIDA_0082021-05-130.0NaNNaNNaNNaNNaNNaN
2953HEAT_AB87EF45A7AF2021-05-2020215Autumn28.0473Central_JHBNaNNaNNaNNaN5011187.02021-05-20Arm B4.3NaNNaNJHB_VIDA_0082021-05-200.0NaNNaNNaNNaNNaNNaN
2954HEAT_DC75B74260C72021-05-2720215Autumn28.0473Central_JHBNaNNaNNaNNaN5020188.02021-05-27Arm D4.7NaNNaNJHB_VIDA_0082021-05-270.0NaNNaNNaNNaNNaNNaN
2955HEAT_680F24B76D612021-06-0820216Winter28.0473Central_JHBNaNNaNNaNNaN5026189.02021-06-08Arm E5.7NaNNaNJHB_VIDA_0082021-06-080.0NaNNaNNaNNaNNaNNaN
2956HEAT_94FAE76823782021-06-0820216Winter28.0473Central_JHBNaNNaNNaNNaN5028190.02021-06-08Arm A5.4NaNNaNJHB_VIDA_0082021-06-080.0NaNNaNNaNNaNNaNNaN
2957HEAT_3044CB8D5B852021-06-0820216Winter28.0473Central_JHBNaNNaNNaNNaN5029191.02021-06-08Arm C4.9NaNNaNJHB_VIDA_0082021-06-080.0NaNNaNNaNNaNNaNNaN